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qwen3.5-9b-deepseek-v4-flash
# Qwen3.5-9B
[](https://chat.qwen.ai)
> [!Note]
> This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency.
## Qwen3.5 Highlights
Qwen3.5 features the following enhancement:
- **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead.
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Repository: localaiLicense: apache-2.0
qwen3.5-9b-glm5.1-distill-v1
# 🪐 Qwen3.5-9B-GLM5.1-Distill-v1
## 📌 Model Overview
**Model Name:** `Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1`
**Base Model:** Qwen3.5-9B
**Training Type:** Supervised Fine-Tuning (SFT, Distillation)
**Parameter Scale:** 9B
**Training Framework:** Unsloth
This model is a distilled variant of **Qwen3.5-9B**, trained on high-quality reasoning data derived from **GLM-5.1**.
The primary goals are to:
- Improve **structured reasoning ability**
- Enhance **instruction-following consistency**
- Activate **latent knowledge via better reasoning structure**
## 📊 Training Data
### Main Dataset
- `Jackrong/GLM-5.1-Reasoning-1M-Cleaned`
- Cleaned from the original `Kassadin88/GLM-5.1-1000000x` dataset.
- Generated from a **GLM-5.1 teacher model**
- Approximately **700x** the scale of `Qwen3.5-reasoning-700x`
- Training used a **filtered subset**, not the full source dataset.
### Auxiliary Dataset
- `Jackrong/Qwen3.5-reasoning-700x`
...
Repository: localaiLicense: apache-2.0
qwen3.5-9b
Repository: localaiLicense: apache-2.0
boomerang-qwen3-4.9b
Boomerang distillation is a phenomenon in LLMs where we can distill a teacher model into a student and reincorporate teacher layers to create intermediate-sized models with no additional training. This is the student model distilled from Qwen3-8B-Base from our paper.
This model was initialized from Qwen3-8B-Base by copying every other layer and the last 2 layers. It was distilled on 2.1B tokens of The Pile deduplicated with cross entropy, KL, and cosine loss to match the activations of Qwen3-8B-Base.
Repository: localaiLicense: apache-2.0
eurollm-9b-instruct
The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages. EuroLLM-9B is a 9B parameter model trained on 4 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets. EuroLLM-9B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
Repository: localaiLicense: apache-2.0
nvidia_llama-3_3-nemotron-super-49b-v1
Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.
Llama-3.3-Nemotron-Super-49B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff.
The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints. For more details on how the model was trained, please see this blog.
Repository: localaiLicense: nvidia-open-model-license
thedrummer_valkyrie-49b-v1
it swears unprompted 10/10 model
... characters work well, groups work well, scenarios also work really well so great model overall
This is pretty exciting though. GLM-4 already had me on the verge of deleting all of my other 32b and lower models. I got to test this more but I think this model at Q3m is the death blow lol
Smart Nemotron 49b learned how to roleplay
Even without thinking it rock solid at 4qm.
Without thinking is like 40-70b level. With thinking is 100+b level
This model would have been AGI if it were named properly with a name like "Bob". Alas, it was not.
I think this model is nice. It follows prompts very well. I didn't really note any major issues or repetition
Yeah this is good. I think its clearly smart enough, close to the other L3.3 70b models. It follows directions and formatting very well. I asked it to create the intro message, my first response was formatted differently, and it immediately followed my format on the second message. I also have max tokens at 2k cause I like the model to finish it's thought. But I started trimming the models responses when I felt the last bit was unnecessary and it started replying closer to that length. It's pretty much uncensored.
Nemotron is my favorite model, and I think you fixed it!!
Repository: localaiLicense: llama3.3
nvidia_llama-3_3-nemotron-super-49b-genrm-multilingual
Llama-3.3-Nemotron-Super-49B-GenRM-Multilingual is a generative reward model that leverages Llama-3.3-Nemotron-Super-49B-v1 as the foundation and is fine-tuned using Reinforcement Learning to predict the quality of LLM generated responses.
Llama-3.3-Nemotron-Super-49B-GenRM-Multilingual can be used to judge the quality of one response, or the ranking between two responses given a multilingual conversation history. It will first generate reasoning traces then output an integer score. A higher score means the response is of higher quality.
Repository: localaiLicense: nvidia-open-model-license
yi-1.5-9b-chat
Yi-1.5-9B-Chat is a quantized GGUF model optimized for local inference. It delivers strong performance in coding, math, and reasoning while maintaining excellent instruction-following capabilities. Suitable for chat and completion tasks on consumer hardware.
Repository: localaiLicense: apache-2.0
master-yi-9b
Master is a collection of LLMs trained using human-collected seed questions and regenerate the answers with a mixture of high performance Open-source LLMs.
Master-Yi-9B is trained using the ORPO technique. The model shows strong abilities in reasoning on coding and math questions.
Repository: localaiLicense: apache-2.0
yi-coder-9b-chat
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
yi-coder-9b
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
Excelling in long-context understanding with a maximum context length of 128K tokens.
Supporting 52 major programming languages:
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
For model details and benchmarks, see Yi-Coder blog and Yi-Coder README.
Repository: localaiLicense: apache-2.0
cursorcore-yi-9b
CursorCore is a series of open-source models designed for AI-assisted programming. It aims to support features such as automated editing and inline chat, replicating the core abilities of closed-source AI-assisted programming tools like Cursor. This is achieved by aligning data generated through Programming-Instruct. Please read our paper to learn more.
Repository: localaiLicense: apache-2.0
flux.2-klein-9b
The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, delivering state-of-the-art quality with end-to-end inference in as low as under a second. Built for applications that require real-time image generation without sacrificing quality, and runs on consumer hardware, with as little as 13GB VRAM.
FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
Repository: localaiLicense: apache-2.0